The present invention employees processes from two fields of endeavor, the first being Natural Language Processing (NLP), and more particularly Natural Language Database Query, which attempts to convert a user's information request entered either vocally, verbally or visually, usually on a browser or mobile device, into a database query command to provide exact answers.
The present invention is also in the field of endeavor of Business Intelligence (B.I.), which employs “database savvy” Information Technology (I.T.) specialists to write database query commands by hand to retrieve desired information to an organization's users.
The two fields of endeavor cited above share a common goal: retrieve desired information from data sources in a timely and accurate manner. Systems and processes in both fields of endeavor face problems that often prevent them from accomplishing their goal, explained below.
In the case of B.I., the labor-intensive approach of having data-savvy I.T. Professionals write query programs by hand takes time: usually hours or days, and thus is not an acceptable approach for returning immediate answers to ad hoc user requests. The era of mobile devices has brought great pressure on organizations to provide immediate answers for their users using Natural Language requests.
The field of NLP, particularly through Speech To Text (STT) software on mobile devices, has its own set of problems. Since current products based on Natural Language user inputs, such as search engines, produce multiple links to documents and other unstructured information sources, they are not generally suitable for extracting “exact answers” and analytics.
The narrower field of Natural Language Data Search also has problems that go back to the early products of this type, introduced in the 1960s and 1970s. One problem was that they worked with only a few database sources. The main problem, though, was that they failed to get correct answers in a great majority of user attempts. As is always the case, if a system or product doesn't produce satisfactory results it's users will quickly abandon it, which is what happened: none of these early products exists today.
Some user goals are so persistent, with a demand so great, that entrepreneurs will keep trying new solutions to meet these user demands. Because of the immense surge in mobile devices, users are even more demanding of systems that will return immediate, direct answers to their information requests. This demand has resulted in the recent product releases of Natural Language Data Search products, namely IBMs' Watson, Apple's siri, and Microsoft's new Power BI product.
Despite these recent Natural Language (“NL”) product announcements, the age-old problem still exists: siri, for example, has a success ratio of only 20% to 30% in getting correct answers from databases, and Google, Bing, Wolfram Alpha and other leading search products have similarly poor ratios of success per user request. The main reason for the overall poor success ratio of Natural Language Query products is, and has always been, that there are many ways to ask for the same fact, and Natural Language “Understanding” methodologies have so far proved incapable of understanding a sufficiently high ratio of user requests.